Tracking Flooding Phase Transitions and Establishing a Passive Hotline With AI-Enabled Social Media Data

Ruo-Qian Wang,Kevin Yang,Zikai Zhou,Yingjie Hu

doi:10.1109/access.2020.2994187

Abstract

Flooding management requires collecting real-time onsite information widely and rapidly. As an emerging data source, social media demonstrates an advantage of providing in-time, rich data in the format of texts and photos and can be used to improve flooding situation awareness. The present study shows that social media data, with additional information processed by Artificial Intelligence (AI) techniques, can be effectively used to track flooding phase transition and locate emergency incidents. To track phase transition, we train a computer vision model that can classify images embedded in social media data into four categories - preparedness, impact, response, and recovery - that can reflect the phases of disaster event development. To locate emergency incidents, we use a deep learning based natural language processing (NLP) model to recognize locations from textual content of tweets. The geographic coordinates of the recognized locations are assigned by searching through a dedicated local gazetteer rapidly compiled for the disaster affected region based on the GeoNames gazetteer and the US Census data. By combining image and text analysis, we filter the tweets that contain images of the “Impact” category and high-resolution locations to gain the most valuable situation information. We carry out a manual examination step to complement the automatic data processing and find that it can further strengthen the AI-processed results to support comprehensive situation awareness and to establish a passive hotline to inform rescue and search activities. The developed framework is applied to the flood of Hurricane Harvey in the Houston area.

Highlights

Urban flooding is becoming a national threat, causing billions of dollars of losses every year
Comparing to the traditional method of water basin-scale flooding analysis, analyzing and predicting urban flooding require data of high spatial and temporal resolution. Such high-quality geo-tagged urban flooding data are challenging to collect, because: remote sensing is limited by certain weather conditions, such as cloud coverage; sensor networks are costly to install and maintain in urban areas; insurance reports are usually inaccessible and delayed in time; and government surveying is limited in coverage and accuracy [4]
There are two main types of data that can be collected through social media, images and texts, which have been used in previous studies

Summary

Introduction

Urban flooding is becoming a national threat, causing billions of dollars of losses every year. Comparing to the traditional method of water basin-scale flooding analysis, analyzing and predicting urban flooding require data of high spatial and temporal resolution. Such high-quality geo-tagged urban flooding data are challenging to collect, because: remote sensing is limited by certain weather conditions, such as cloud coverage; sensor networks are costly to install and maintain in urban areas; insurance reports are usually inaccessible and delayed in time; and government surveying is limited in coverage and accuracy [4]. Jongman et al [5] analyzed the text of flood-related tweets sent by disaster response organizations to understand the location, timing, causes, and impacts of floods.

Methods

Results

Discussion

Conclusion